An Effective Hybrid Approach Based on Machine Learning Techniques for Auto-Translation: Japanese to English

Sharif, Mhd Saeed; Romo, Bilyaminu Auwal; Maltby, Harry; Al-Bayatti, Ali H.

doi:10.1109/3ict53449.2021.9581629

Cited by 2 publications

(1 citation statement)

References 13 publications

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“…R. Abdul Wahid et al, 2019;Freedman, 2018;Freedman & Slade, 2018), as well as gender and sexualities (Chan, 2017;Darlington & Cooper, 2010;Lewicki, 2022;Ting, 2019), among numerous others. Within the realm of translation studies, research on manga translations and scanlations (Sell, 2011) ranges from issues surrounding localisations (Hevia, 2007;Maser, 2020;Schodt, 2016), adaptation (Zanettin, 2014(Zanettin, , 2018, danmaku subtitling or online collaborative translation (Yang, 2021), automated or machine translations (Hinami et al, 2021;Sharif et al, 2021), use of notes in scanlations (Fabbretti, 2016;Okyayuz, 2017) and manga translations as a pedagogical tool (Furuhata-turner, 2013;Rohan et al, 2021). In the Malay/ Bahasa Indonesiaspeaking region, studies on manga translation cover the translation of humour and onomatopoeia in manga (Chow & Che Omar, 2019), as well as aspects of translation procedures and intercultural communication Ningsih & Hidayat, 2018;Salsabila & Laksman-Huntley, 2020).…”

Section: Introductionmentioning

confidence: 99%

Exploring Translation Strategies of Japanese Manga in Google Translate and Komikcast Translations

Yusof,

Basuni

2024

Arbitrer

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This paper examines the translations of ‘Golden Kamuy Vol. 1’ from Japanese to Bahasa Melayu/Bahasa Indonesia by MT system Google Translate (GT) and online fan-based translating community Komikcast. Given the linguistic and sociocultural differences between the source language (SL) and target language (TL), translating manga demands expertise and a profound understanding of the cultural nuances embedded in the SL, the specific manga genre, and the target audience. This qualitative descriptive study uses Vinay and Darbelnet’s (1958/1995) translation strategies to analyze the translations of ‘Golden Kamuy Vol. 1’ by GT and Komikcast. The study finds that both platforms employ Literal Translation, Borrowing, and Equivalence strategies, with Komikcast additionally using Modulation. GT primarily relies on literary translation, which is consistent with its algorithmic nature and reliance on translation memories. Conversely, Komikcast emphasizes the Equivalence strategy, recognizing the significance of language proficiency and the contextual and cultural insights human translators provide. Findings are discussed in relation to Nida’s equivalence effects, particularly regarding the translated text’s purpose, translators’ knowledge, and cultural differences in SL and TL. This research enhances our understanding of manga translation, as well as the strengths and limitations of machine and human translation, while contributing to the broader discussion on translation studies. While acknowledging the role and potential of machine translation, this research also reinforces the critical role of human translators in bridging linguistic and cultural gaps between Japanese and Indonesian, highlighting the need for translators to possess deep cultural insights and proficiency beyond literal translations for more authentic and contextually accurate renditions.

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Section: Introductionmentioning

confidence: 99%

Exploring Translation Strategies of Japanese Manga in Google Translate and Komikcast Translations

Yusof,

Basuni

2024

Arbitrer

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Application of non-negative matrix factorization in oncology: one approach for establishing precision medicine

Hamamoto

Takasawa

Machino

et al. 2022

Briefings in Bioinformatics

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The increase in the expectations of artificial intelligence (AI) technology has led to machine learning technology being actively used in the medical field. Non-negative matrix factorization (NMF) is a machine learning technique used for image analysis, speech recognition, and language processing; recently, it is being applied to medical research. Precision medicine, wherein important information is extracted from large-scale medical data to provide optimal medical care for every individual, is considered important in medical policies globally, and the application of machine learning techniques to this end is being handled in several ways. NMF is also introduced differently because of the characteristics of its algorithms. In this review, the importance of NMF in the field of medicine, with a focus on the field of oncology, is described by explaining the mathematical science of NMF and the characteristics of the algorithm, providing examples of how NMF can be used to establish precision medicine, and presenting the challenges of NMF. Finally, the direction regarding the effective use of NMF in the field of oncology is also discussed.

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An Effective Hybrid Approach Based on Machine Learning Techniques for Auto-Translation: Japanese to English

Cited by 2 publications

References 13 publications

Exploring Translation Strategies of Japanese Manga in Google Translate and Komikcast Translations

Exploring Translation Strategies of Japanese Manga in Google Translate and Komikcast Translations

Application of non-negative matrix factorization in oncology: one approach for establishing precision medicine

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